
import tensorflow as tf
import numpy as np

# create data
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data *0.1 + 0.3


Weigths = tf.Variable(tf.random_uniform([1], -1.0,1.0))
biases = tf.Variable(tf.zeros([1]))

y = Weigths*x_data+biases
loss = tf.reduce_mean(tf.square(y-y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    for step in range(201):
        sess.run(train)
        if step %20 == 0:
            print(step,sess.run(Weigths), sess.run(biases))
